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Osteosarcoma is the most common type of primary malignant bone tumor. Due to the lack of selectivity and sensitivity of chemotherapy drugs to tumor cells, coupled with the use of large doses, chemotherapy drugs often have systemic toxicity. The use of modern sequencing technology to screen tumor markers in a large number of tumor samples is a common method for screening highly specific and selective anti-tumor drugs. This study aims to identify potential biomarkers using the latest reported gene expression signatures of oncogene-induced replication stress (ORS) in aggressive cancers, and potential anti-osteosarcoma drugs were screened in different drug databases. In this study, we obtained 89 osteosarcoma-related samples in the TARGET database, all of which included survival information. According to the median expression of each of six reported ORS gene markers (NAT10/DDX27/ZNF48/C8ORF33/MOCS3/MPP6), we divided 89 osteosarcoma gene expression datasets into a high expression group and a low expression group and then performed a differentially expressed gene (DEG) analysis. The coexisting genes of 6 groups of DEGs were used as replication stress-related genes (RSGs) of osteosarcoma. Then, key RSGs were screened using LASSO regression, a Cox risk proportional regression prognostic model and a tenfold cross-validation test. GSE21257 datasets collected from the Gene Expression Omnibus (GEO) database were used to verify the prognostic model. The final key RSGs selected were used in the L1000PWD and DGIdb databases to mine potential drugs. After further validation by the prognostic model, we identified seven genes associated with ORS in osteosarcoma as key RSGs, including transcription factor 7 like 2 (TCF7L2), solute carrier family 27 member 4 (SLC27A4), proprotein convertase subtilisin/kexin type 5 (PCSK5), nucleolar protein 6 (NOL6), coiled-coil-coil-coil-coil-helix domain containing 4 (CHCHD4), eukaryotic translation initiation factor 3 subunit B (EIF3B), and synthesis of cytochrome C oxidase 1 (SCO1). Then, we screened the seven key RSGs in two drug databases and found six potential anti-osteosarcoma drugs (D GIdb database: repaglinide, tacrolimus, sirolimus, cyclosporine, and hydrochlorothiazide; L1000PWD database: the small molecule VU-0365117-1). Seven RSGs (TCF7L2, SLC27A4, PCSK5, NOL6, CHCHD4, EIF3B, and SCO1) may be associated with the ORS gene signatures in osteosarcoma. Repaglinide, tacrolimus, sirolimus, cyclosporine, hydrochlorothiazide and the small molecule VU-0365117-1 are potential therapeutic drugs for osteosarcoma. [ABSTRACT FROM AUTHOR]

Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model's superiority. [ABSTRACT FROM AUTHOR]

The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation. [ABSTRACT FROM AUTHOR]

Attention mechanism, which is a cognitive process of selectively concentrating on certain information while ignoring others, has been successfully employed in deep learning. In this paper, we introduce the attention mechanism into a particle swarm optimizer and propose an attention-based particle swarm optimizer (APSO) for large scale optimization. In the proposed method, the attention mechanism is introduced such that activating different particles to participate in evolution at different stages of evolution. Further, an attention-based particle learning is devised to randomly select three particles from a predominant sub-swarm, which is activated by the attention mechanism, to guide the learning of particles. The cooperation of these two strategies could be employed to achieve a balanced evolution search, thus appropriately searching the space of large-scale optimization problems. Extensive experiments have been carried out on CEC'2010 and CEC'2013 large scale optimization benchmark functions to evaluate the performance of proposed method and to compare with related methods. The results show the superiority of proposed method. [ABSTRACT FROM AUTHOR]

To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x-axis and y-axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x-axis and y-axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions. [ABSTRACT FROM AUTHOR]

The literature has provided inconsistent findings on the relationship between interleukin IL-1 gene polymorphisms and susceptibility to ankylosing spondylitis (AS). Therefore, a systematic review and meta-analysis were conducted. Online electronic database searches were performed for relevant research published as of May 2021. Meta-analysis was performed to compare alleles and multiple genetic models (including dominant, recessive, heterozygous, and homozygous models) using random-effects models to reduce the impact of heterogeneity. A 95% confidence interval (95% CI) odds ratio (OR) was used to assess potential relationships. Nineteen studies including 6235 patients with AS and 5919 healthy controls were recruited. IL-1A-889 (rs1800587) had statistical significance in the allelic model (OR 1.38, 95% CI 1.08–1.77, P = 0.010) (I2 = 51%.1, P = 0.0001); homozygous model (OR 1.92, 95% CI 1.27–2.89, P = 0.002); heterozygous model (OR 1.49, 95% CI 1.02–2.17, P = 0.163); dominant genetic model (OR 1.53, 95% CI 1.05–2.24, P = 0.026); and recessive model (OR 1.54, 95% CI 1.04–2.28, P = 0.031). Further stratified analysis showed that the allele model (OR 1.35, 95% CI 1.08–1.69, P = 0.008), heterozygous model (OR 1.45, 95% CI 1.07–1.96, P = 0.017), and dominant model (OR 1.49, 95% CI 1.11–1.99, P = 0.007) in the English population and allele model (OR 2.21, 95% CI 1.45–3.37, P = 0.0001), homozygous model (OR 3.85, 95% CI 1.38–10.76, P = 0.010), heterozygous model (OR 3.42, 95% CI 1.85–6.32, P = 0.0001), and dominant model (OR 3.49, 95% CI 1.93–6.30, P = 0.001) in Tunis were significantly associated with susceptibility to AS. Analysis of the IL1F7 exon 2 (rs3811047) showed that the G allele frequency was higher in the normal population than in the AS population (OR 0.76, 95% CI (0.64, 0.91)). Further stratified analysis concluded that the allele model was significantly associated with AS susceptibility in Canadian (OR 0.76, 95% CI 0.61–0.94, P = 0.011) and Chinese patients (OR 0.64, 95% CI 0.41–0.98, P = 0.041). The meta-analysis showed that the IL-1 gene polymorphism IL-1A-889 (rs1800587) increases the risk of AS in English and Tunisian populations. IL1F7 exon 2 (rs3811047) is negatively correlated with susceptibility to AS in Canadian and Chinese populations, but additional studies are needed for further exploration. [ABSTRACT FROM AUTHOR]

Purpose: The Fremantle back awareness questionnaire (FreBAQ) was recently developed as simple and quick tool to assess back-specific body perception in Low back pain (LBP) patients. The aim of the present study was to translate and cross-culturally adapt the Fremantle back awareness questionnaire (FreBAQ) into a Simplified Chinese version (FreBAQ-C), and evaluate the reliability and validity of the FreBAQ-C in patients with non-specific Chronic Low back pain (CLBP). Methods: The FreBAQ was translated into Chinese according to established methods. Internal consistency was assessed according to Cronbach's alpha. Test–retest reliability was estimated by Intraclass correlation coefficient (ICC). Construct validity was evaluated by correlations between the FreBAQ-C and Visual analogue scale (VAS), Roland-Morris disability questionnaire (RDQ), Pain catastrophizing scale (PCS), Tampa scale for kinesiophobia (TSK) as well as Hospital anxiety and depression scale (HADS). Results: A total of 105 participants (38 males and 67 females) were included in this study with the mean age of 54.1 ± 15.6 years, mean duration of LBP of 6.8 ± 4.6 years. The FreBAQ-C total scores were well distributed, with no floor or ceiling effects. Internal consistency was excellent (Cronbach's alpha = 0.833). ICC of test–retest reliability was good (0.897, 95% confidence interval: 0.852–0.929). The limits of agreement (LOA) ranged from − 5.8 to 6.3. The Standard error of measurement (SEM) and Minimum detectable change (MDC) were 2.16 and 5.99. Construct validity was confirmed by significant correlation of The FreBAQ-C and VAS during motion (r = 0.274, p = 0.005) and rest (r = 0.243, p = 0.012), RDQ (r = 0.377, p < 0.001), PCS (r = 0.439, p < 0.001), and TSK(r = 0.311, p = 0.001). Conclusions: The FreBAQ-C was demonstrated to have acceptable reliability and validity for patients with non-specific CLBP in Chinese mainland. It will allow evaluating body preception of the back in the Chinese population with CLBP. [ABSTRACT FROM AUTHOR]

In this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique. [ABSTRACT FROM AUTHOR]

As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset. [ABSTRACT FROM AUTHOR]

In this paper, a deep belief network (DBN) is employed to deal with the problem of the patient attendance disposal in accident & emergency (A&E) departments. The selection of the hyperparameters of the employed DBN is automated by using the particle swarm optimization (PSO) algorithm that is known for its simplicity, easy implementation and relatively fast convergence rate to a satisfactory solution. Specifically, a recently developed randomly occurring distributedly delayed PSO (RODDPSO) algorithm, which is capable of seeking the optimal solution and alleviating the premature convergence, is exploited with aim to optimize the hyperparameters of the DBN. The developed RODDPSO-based DBN is successfully applied to analyze the A&E data for classifying the patient attendance disposal in the A&E department of a hospital in west London. Experimental results show that the proposed RODDPSO-based DBN outperforms the standard DBN and the modified DBN in terms of the classification accuracy. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders the global optima from being found. In this paper, a novel randomized particle swarm optimizer (RPSO) is proposed where the Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order for the problem space to be explored more thoroughly. With this new strategy, the RPSO algorithm not only maintains the population diversity but also enhances the possibility of escaping the local optima trap. Experimental results demonstrate that the proposed RPSO algorithm outperforms some existing popular variants of PSO algorithms on a series of widely used optimization benchmark functions. [ABSTRACT FROM AUTHOR]

The Chinese space station will be built around 2020. As a national space laboratory, it will offer unique opportunities for studying the physiological effects of weightlessness and the efficacy of the countermeasures against such effects. In this paper, we described the development of countermeasure systems in the Chinese space program. To emphasize the need of the Chinese space program to implement its own program for developing countermeasures, we reviewed the literature on the negative physiological effects of weightlessness, the challenges of completing missions, the development of countermeasure devices, the establishment of countermeasure programs, and the efficacy of the countermeasure techniques in American and Russian manned spaceflights. In addition, a brief overview was provided on the Chinese research and development on countermeasures to discuss the current status and goals of the development of countermeasures against physiological problems associated with weightlessness. [ABSTRACT FROM AUTHOR]

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today’s mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. [ABSTRACT FROM AUTHOR]

Purpose: Multi-level reconstruction incorporating the chest wall and ribs is technically demanding after multi-segmental total en bloc spondylectomy (TES) of thoracic spinal tumours. Few surgical techniques are reported for effective reconstruction. A novel and straightforward technical reconstruction through posterior-lateral approach was presented to solve the extensive chest wall defect and prevent occurrences of severe respiratory dysfunctions after performing TES. The preliminary outcomes of surgery were reviewed.Methods: Multi-level TES was performed for five patients with primary or recurrent thoracic spinal malignancies through posterior-lateral approach. The involved ribs and chest wall were removed to achieve tumour-free margin. Then titanium mesh with allograft bone and pedicle screw-rod system were adopted for the circumferential spinal reconstruction routinely. Titanium rods were modified accordingly to attach to the screw-rod system proximally, and the distal end of rods was dynamically inserted into the ribs.Results: The mean surgery time was 6.7 hours (range 5-8), with the average blood loss of 3260 ml (range 2300-4500). No severe neurological complications were reported while three patients had complaints of slight numbness of chest skin (no. 1, 3, and 5). No severe respiratory complications occurred during peri-operative period. No implant failure and no local recurrence or distant metastases were observed with an average follow-up of 12.5 months.Conclusions: The single-stage reconstructions incorporating spine and chest wall are straightforward and easy to perform. The preliminary outcomes of co-reconstructions are promising and favourable. More studies and longer follow-up are required to validate this technique. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of geohazards on the existing oil pipeline and the potential interaction between the proposed new oil pipeline and preexisting transportation structures along the Qinghai-Tibet Plateau Engineering Corridor. The current Golmud-Lhasa oil pipeline has been seriously affected by retrogressive thaw slumps caused by surface water being channeled through culverts causing serious erosion problems. Climate data show that the air temperature increased at a rate of 0.0281 °C/a for the past 60 years along the corridor. To design the new pipeline, the effects of revegetation, climate warming and pipe insulation on permafrost have been simulated using numerical modeling. A warm oil pipeline would potentially lead to significant thawing of the permafrost foundation. When climate warming is not considered, insulation of the buried pipe could keep the permafrost stable. Revegetation and the use of utilidors could counteract the influence of heat input from the oil pipe, and even a 1.1 °C/50a climate-warming rate. However, for the 2.6 °C/50a climate-warming-rate scenario, they are inadequate to keep the permafrost stable. Vegetation cover is important to reduce the effect of climate warming on both the natural and the human-impacted permafrost. Revegetation after construction is important to protect the permafrost environment as well as the oil pipeline itself. [ABSTRACT FROM AUTHOR]

Gold immunochromatographic strip (GICS) has become a popular membrane-based diagnostic tool in a variety of settings due to its sensitivity, simplicity and rapidness. This paper aimed to develop a framework of automatic image inspection to further improve the sensitivity as well as the quantitative performance of the GICS systems. As one of the latest methodologies in machine learning, the deep belief network (DBN) is applied, for the first time, to quantitative analysis of GICS images with hope to segment the test and control lines with a high accuracy. It is remarkable that the exploited DBN is capable of simultaneously learning three proposed features including intensity, distance and difference to distinguish the test and control lines from the region of interest that are obtained by preprocessing the GICS images. Several indices are proposed to evaluate the proposed method. The experiment results show the feasibility and effectiveness of the DBN in the sense that it provides a robust image processing methodology for quantitative analysis of GICS. [ABSTRACT FROM AUTHOR]

Introduction: Gitelman syndrome(GS) is a rare inherited tubular disorder which is characterized by hypokalemia, metabolic alkalosis, hypomagnesemia, and hypocalciuria. Here, we report a case of schizophrenia-like psychosis concomitant with GS and related literatures are reviewed. Case description: An 18-year-old male patient with 1-week history of auditory hallucinations, sense of insecurity, delusions of reference and feelings of being followed and controlled by others unknown, insomnia was admitted to Psychiatry department in December, 2013. Hypokalemia and hypomagnesemia were noted. He was diagnosed as schizophrenia-like psychosis. Treatment with paliperidone at the dose of 6 mg/day and magnesium and potassium supplementations was commenced. However, electrolyte disturbances failed to improve following psychosis remission. Therefore, other underlying diseases resulting in electrolyte disturbances were suspected. Along with hypokalemia and hypomagnesemia, additional investigation showing metabolic alkalosis, hypocalciuria, renal loss of potassium, were consistent with GS. Gene analysis revealed this patient carried out c. 2687 G > A homozygous mutation of exon 23 in the SLC12A3 gene which led to p.Arg896Gln. Eventually, GS was identified. Thus, additional spironolactone (40 mg/day) combined with increased doses of oral potassium chloride sustained-release tablets (3.0 g/day) and potassium magnesium aspartate (0.3 g/day) were administered. During next half a year, fatigue resolved, paliperidone gradually tapered and eventually discontinued while psychosis maintained complete remission. His serum potassium was near normal (3.2-3.5 mmol/L), hypomagnesemia significantly improved (0.57-0.67 mmol/L). Discussion and evaluation: Electrolyte abnormalities secondary to GS might cause or contribute to development of neuropsychiatric symptoms. In turn, hypokalemia was common among acute psychiatric inpatients. As a consequence, when concomitant with psychosis, GS was readily concealed. Conclusion: Electrolyte disturbances are common in acute psychiatric patients. However, when electrolyte disturbances are not improved following psychosis remission, other underlying diseases such as GS should be considered. [ABSTRACT FROM AUTHOR]

Methylphenidate (MPH) is one of the most commonly used stimulants for the treatment of attention deficit hyperactivity disorder (ADHD). Although several studies have evaluated the effects of MPH on human brain activation during specific cognitive tasks using functional magnetic resonance imaging (fMRI), few studies have focused on spontaneous brain activity. In the current study, we investigated the effect of MPH on the intra-regional synchronization of spontaneous brain activity during the resting state in 18 normal adult males. A handedness questionnaire and the Wechsler Adult Intelligence Scale were applied before medication, and a resting-state fMRI scan was obtained 1 h after medication (20 mg MPH or placebo, order counterbalanced between participants). We demonstrated that: (1) there were no significant differences in the performance of behavioral tasks between the MPH and placebo groups; (2) the left middle and superior temporal gyri had stronger MPHrelated regional homogeneity (ReHo); and (3) the left lingual gyrus had weaker MPH-related ReHo. Our findings showed that the ReHo in some brain areas changes with MPH compared to placebo in normal adults, even though there are no behavioral differences. This method can be applied to patients with mental illness who may be treated with MPH, and be used to compare the difference between patients taking MPH and normal participants, to help reveal the mechanism of how MPH works. [ABSTRACT FROM AUTHOR]

Using the microwave sintering technology, the effects of phosphorus (P) additions on the microstructure and properties of the ultrafine WC-10Co alloys were investigated. The experimental results show that with only 0.3wt% P additions, full density WC-10Co cermets were obtained at temperature of 1250 °C, which is 70 °C lower than that of the undoped counterparts. Lower sintering temperature can result in finer WC grain growth; therefore, the P-doped WC-10Co alloys exhibited higher hardness than the undoped ones. But at the same time, P doping could lead to sacrifice of fracture toughness of WC-10Co cemented carbides. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Methods: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Results: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Conclusion: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition. [ABSTRACT FROM AUTHOR]